SF patch 629637: Add sample(population, k) method to the random module.

Used for random sampling without replacement.
This commit is contained in:
Raymond Hettinger 2002-11-12 17:41:57 +00:00
parent 3a7ad5c584
commit f24eb35d18
3 changed files with 78 additions and 2 deletions

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@ -179,6 +179,25 @@ Functions for sequences:
long sequence can never be generated.
\end{funcdesc}
\begin{funcdesc}{sample}{population, k}
Return a \var{k} length list of unique elements chosen from the
population sequence. Used for random sampling without replacement.
Returns a new list containing elements from the population. The
list itself is in random order so that all sub-slices are also
random samples. The original sequence is left undisturbed.
If the population has repeated elements, then each occurence is a
possible selection in the sample.
If indices are needed for a large population, use \function{xrange}
as an argument: \code{sample(xrange(10000000), 60)}.
Optional argument random is a 0-argument function returning a random
float in [0.0, 1.0); by default, the standard random.random.
\versionadded{2.3}
\end{funcdesc}
The following functions generate specific real-valued distributions.
Function parameters are named after the corresponding variables in the

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@ -7,6 +7,7 @@
sequences
---------
pick random element
pick random sample
generate random permutation
distributions on the real line:
@ -77,7 +78,7 @@ from math import log as _log, exp as _exp, pi as _pi, e as _e
from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
from math import floor as _floor
__all__ = ["Random","seed","random","uniform","randint","choice",
__all__ = ["Random","seed","random","uniform","randint","choice","sample",
"randrange","shuffle","normalvariate","lognormvariate",
"cunifvariate","expovariate","vonmisesvariate","gammavariate",
"stdgamma","gauss","betavariate","paretovariate","weibullvariate",
@ -373,6 +374,43 @@ class Random:
j = int(random() * (i+1))
x[i], x[j] = x[j], x[i]
def sample(self, population, k, random=None, int=int):
"""Chooses k unique random elements from a population sequence.
Returns a new list containing elements from the population. The
list itself is in random order so that all sub-slices are also
random samples. The original sequence is left undisturbed.
If the population has repeated elements, then each occurence is
a possible selection in the sample.
If indices are needed for a large population, use xrange as an
argument: sample(xrange(10000000), 60)
Optional arg random is a 0-argument function returning a random
float in [0.0, 1.0); by default, the standard random.random.
"""
n = len(population)
if not 0 <= k <= n:
raise ValueError, "sample larger than population"
if random is None:
random = self.random
if n < 6 * k: # if n len list takes less space than a k len dict
pool = list(population)
for i in xrange(n-1, n-k-1, -1):
j = int(random() * (i+1))
pool[i], pool[j] = pool[j], pool[i]
return pool[-k:]
inorder = [None] * k
selections = {}
for i in xrange(k):
j = int(random() * n)
while j in selections:
j = int(random() * n)
selections[j] = inorder[i] = population[j]
return inorder # return selections in the order they were picked
## -------------------- real-valued distributions -------------------
## -------------------- uniform distribution -------------------
@ -711,7 +749,19 @@ def _test_generator(n, funccall):
print 'avg %g, stddev %g, min %g, max %g' % \
(avg, stddev, smallest, largest)
def _test(N=20000):
def _test_sample(n):
# For the entire allowable range of 0 <= k <= n, validate that
# the sample is of the correct length and contains only unique items
population = xrange(n)
for k in xrange(n+1):
s = sample(population, k)
assert len(dict([(elem,True) for elem in s])) == len(s) == k
def _sample_generator(n, k):
# Return a fixed element from the sample. Validates random ordering.
return sample(xrange(n), k)[k//2]
def _test(N=2000):
print 'TWOPI =', TWOPI
print 'LOG4 =', LOG4
print 'NV_MAGICCONST =', NV_MAGICCONST
@ -735,6 +785,9 @@ def _test(N=20000):
_test_generator(N, 'betavariate(3.0, 3.0)')
_test_generator(N, 'paretovariate(1.0)')
_test_generator(N, 'weibullvariate(1.0, 1.0)')
_test_generator(N, '_sample_generator(50, 5)') # expected s.d.: 14.4
_test_generator(N, '_sample_generator(50, 45)') # expected s.d.: 14.4
_test_sample(1000)
# Test jumpahead.
s = getstate()
@ -760,6 +813,7 @@ uniform = _inst.uniform
randint = _inst.randint
choice = _inst.choice
randrange = _inst.randrange
sample = _inst.sample
shuffle = _inst.shuffle
normalvariate = _inst.normalvariate
lognormvariate = _inst.lognormvariate

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@ -427,6 +427,9 @@ Library
- Added operator.pow(a,b) which is equivalent to a**b.
- Added random.sample(population,k) for random sampling without replacement.
Returns a k length list of unique elements chosen from the population.
- random.randrange(-sys.maxint-1, sys.maxint) no longer raises
OverflowError. That is, it now accepts any combination of 'start'
and 'stop' arguments so long as each is in the range of Python's